Spatial resolution, also referred to as ground sample distance (GSD), is one of the key parameters in design and building of an imaging satellite sensor. Satellite data users typically prefer to receive images with high spatial resolution in order to better serve their applications. Designing and building a satellite sensor with a considerably high spatial resolution can be prohibitively expensive and/or constrained by technology availability.
Image fusion is one way to increase spatial resolution of satellite images. Multiple images of the same scene observed by the same sensor at different times or observed by different sensors at either the same time or different times are fused to attain a high spatial resolution image. This is explained in C. Pohl and J. L. Van Genderen, “Multisensor image fusion in remote sensing: concept, methods and applications.” Int. J. Remote Sensing, Vol. 19, No. 5, pp. 823-854, 1998, hereinafter “Pohl et al.”. In the case of multispectral or hyperspectral sensors, spatial resolution of multispectral or hyperspectral imageries can be enhanced by fusing the low resolution multispectral or hyperspectral imageries with a high spatial resolution panchromatic (PAN) image that was acquired simultaneously by the PAN instrument onboard the same satellite. However, the image fusion based spatial resolution enhancement approach requires multiple observed images of the same scene, or the high spatial resolution PAN image being available. In practice, these images may not be always available. Even if the multiple images of the same scene or the high spatial resolution PAN image are available, it is a nontrivial task to fuse the images to precisely enhance the spatial resolution. For example, the orbit, review angle and weather conditions may change the appearance of an area between passes of a satellite over the area.
An accurate geometric registration and the radiometric normalization of the images to be fused are crucial to image fusion, since the multiple images of the same scene acquired by different sensors or by the same sensor at different times are inconsistent, as explained in P. R. Coppin and M. E Bauer, “Digital change detection in forest ecosystems with remote sensing imagery.” Remote Sensing of Reviews, 13, 207-234, 1996. The multiple images may not have a common geometric base and a common radiometric base. Without a common geometric base, the multiple images of the same scene are not associated with each other for the spatial information. This makes precise spatial resolution enhancement difficult. As explained in D. P. Roy, “The impact of misregistration upon composited wide field of view satellite data and implications for change detection.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 38 No. 4, 2017-2032, 2000, the fidelity of the fused images is dependent on the accurate registration of the multiple images to a common spatial framework. Image registration techniques have been developed for a long time. However, to date, it is still rare to find an accurate, robust and automatic image registration technique. Manual registration remains by far the most common way to accurately align images, although it is often time consuming and inaccurate, as indicated, for example, in I. Zavorin and J. Le Moigne, “Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery.” IEEE Transactions on Image Processing, Vol. 14, No. 6, pp. 770-782, 2005.
Without a common radiometric base, it is difficulty to fuse the multiple images of the same scene that were acquired at different illumination and atmospheric conditions, view angles, or sensor parameters, because these variations cause pixel intensity difference in the images, while this difference does not reflect the actual object difference in the scene. The multiple images of the same scene need to be well normalized to a common radiometric framework. Inaccurate radiometric normalization of the multiple images of the same scene severely compromises the quality of the fused image.